Risk-Sensitive Code Generation for Secure Neural Model Training

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Solution Overview

Problem

Existing automated code generation models are outdated and unable to adapt to rapidly changing risks, such as security vulnerabilities and industry practices, leading to insecure or inefficient code generation.

Innovation Solution

A risk-sensitive code generation computing system that evaluates request attributes and risk levels to modify a trained neural network model, selecting appropriate training resources to generate risk-sensitive code that addresses identified risks.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If automated code generation models are used to improve delivery speed, then productivity increases, but the models become outdated and generate insecure or inefficient code due to inability to adapt to rapidly changing risks

Engineering Contradiction:

Improvecode delivery speedVSAvoidcode security and quality

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The code generation model is transformed from a static, pre-trained system to a dynamic system that continuously adapts its training resources based on real-time risk assessments. The system dynamically selects and integrates relevant training data from multiple sources (security vulnerability databases, industry best practices, organizational standards) to maintain code quality while enabling rapid generation.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system performs preliminary risk assessment and training resource selection before code generation occurs. By pre-identifying relevant security vulnerabilities, best practices, and organizational standards that apply to a code generation request, the system ensures that the neural network is trained on appropriate data beforehand, preventing security issues and inefficiencies in the generated code.

Inventive Principle:

Principle #10Preliminary action

2Reliability

If specialists continuously update on all risk factors to maintain code quality, then code security improves, but the complexity of tracking rapidly changing events becomes difficult for persons to comprehend

Engineering Contradiction:

Improvecode securityVSAvoidcomplexity of tracking risk events

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The neural network model serves as an intermediary between the complex ecosystem of risk factors (security vulnerabilities, best practices, standards) and the code generation process. Instead of requiring specialists to manually track and comprehend all risk events, the system automatically processes this information through the trained model, which integrates multiple data sources and applies them to generate secure, compliant code.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system implements continuous feedback loops where code generation requests trigger risk assessments, which then feed into training resource selection, and subsequent code generation incorporates lessons from identified risks. This automated feedback mechanism replaces manual tracking by continuously updating the model's understanding of current risks based on new security vulnerabilities, best practices, and organizational standards.

Inventive Principle:

Principle #23Feedback

3Ease of operation

If delayed recognition of risk events occurs to reduce tracking complexity, then ease of operation improves, but code errors and security vulnerabilities increase

Engineering Contradiction:

Improveease of code developmentVSAvoidsecurity vulnerabilities and code errors

Core Design Contradiction:

Ease of operationVSObject-affected harmful factors

Solution Approach 1:

The system maintains continuous risk assessment and training resource integration throughout the code generation process. Rather than periodic updates or manual reviews, the neural network continuously incorporates relevant security vulnerabilities, best practices, and organizational standards as it generates code. This continuous adaptation ensures that even rapidly emerging risks are addressed without requiring complex manual tracking or causing development delays.

Inventive Principle:

Principle #20Continuity of useful action

Data Source

PatentUS12511403B2Risk-adjustment techniques for generating software code data

Publication Date: 2025.12.30 WELLS FARGO BANK NA

AI summary

A risk-sensitive code generation computing system receives query data indicating a requested code section. A code attribute evaluation module determines attributes that indicate characteristics of the query data or the requested code section. Based on the attributes, the code attribute evaluation module calculates risk level data for the requested code section. A code generation module selects, based on the risk level data, a set of training resources. Based on the selected training resources, the risk-sensitive code generation computing system modifies training of a trained neural network model. The modified trained neural network model generates a risk-sensitive code data object based on the requested code section and the risk level data. The risk-sensitive code generation computing system provides at least a portion of the risk-sensitive code data object to an additional computing system.